TY - JOUR
T1 - Modeling of a Bioengineered Immunomodulating Microenvironment for Cell Therapy
AU - Capuani, Simone
AU - Campa-Carranza, Jocelyn Nikita
AU - Hernandez, Nathanael
AU - Chua, Corrine Ying Xuan
AU - Grattoni, Alessandro
N1 - Funding Information:
The authors thanked Dr. Nicola Di Trani and Dr. Prashant Dogra for useful discussions. Funding support from NIH NIDDK R01DK132104 (A.G.), R01DK133610 (A.G.), JDRF 2‐SRA‐2022‐1224‐S‐B (A.G.), JDRF 2‐SRA‐2021‐1078‐S‐B (A.G.), Vivian Smith Foundation (A.G.), Men of Distinction (A.G., C.Y.X.C.). S.C., C.Y.X.C., and A.G. are inventors of intellectual property licensed by NanoGland. A.G. is a cofounder of NanoGland. The other authors declare no conflict of interest. Figure 1 , Figure 5A , illustrations in Figure 7 and the table of content image were created with Biorender.com.
Publisher Copyright:
© 2024 The Authors. Advanced Healthcare Materials published by Wiley-VCH GmbH.
PY - 2024/1/12
Y1 - 2024/1/12
N2 - Cell delivery and encapsulation platforms are under development for the treatment of Type 1 Diabetes among other diseases. For effective cell engraftment, these platforms require establishing an immune-protected microenvironment as well as adequate vascularization and oxygen supply to meet the metabolic demands of the therapeutic cells. Current platforms rely on 1) immune isolating barriers and indirect vascularization or 2) direct vascularization with local or systemic delivery of immune modulatory molecules. Supported by experimental data, here a broadly applicable predictive computational model capable of recapitulating both encapsulation strategies is developed. The model is employed to comparatively study the oxygen concentration at different levels of vascularization, transplanted cell density, and spatial distribution, as well as with codelivered adjuvant cells. The model is then validated to be predictive of experimental results of oxygen pressure and local and systemic drug biodistribution in a direct vascularization device with local immunosuppressant delivery. The model highlights that dense vascularization can minimize cell hypoxia while allowing for high cell loading density. In contrast, lower levels of vascularization allow for better drug localization reducing systemic dissemination. Overall, it is shown that this model can serve as a valuable tool for the development and optimization of platform technologies for cell encapsulation.
AB - Cell delivery and encapsulation platforms are under development for the treatment of Type 1 Diabetes among other diseases. For effective cell engraftment, these platforms require establishing an immune-protected microenvironment as well as adequate vascularization and oxygen supply to meet the metabolic demands of the therapeutic cells. Current platforms rely on 1) immune isolating barriers and indirect vascularization or 2) direct vascularization with local or systemic delivery of immune modulatory molecules. Supported by experimental data, here a broadly applicable predictive computational model capable of recapitulating both encapsulation strategies is developed. The model is employed to comparatively study the oxygen concentration at different levels of vascularization, transplanted cell density, and spatial distribution, as well as with codelivered adjuvant cells. The model is then validated to be predictive of experimental results of oxygen pressure and local and systemic drug biodistribution in a direct vascularization device with local immunosuppressant delivery. The model highlights that dense vascularization can minimize cell hypoxia while allowing for high cell loading density. In contrast, lower levels of vascularization allow for better drug localization reducing systemic dissemination. Overall, it is shown that this model can serve as a valuable tool for the development and optimization of platform technologies for cell encapsulation.
KW - cell encapsulation
KW - computational modeling
KW - islet transplantation
KW - local immunomodulation
KW - vascularization
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U2 - 10.1002/adhm.202304003
DO - 10.1002/adhm.202304003
M3 - Article
C2 - 38215451
AN - SCOPUS:85182715582
SN - 2192-2640
SP - e2304003
JO - Advanced Healthcare Materials
JF - Advanced Healthcare Materials
ER -